Machine Learning Tutorials

Welcome to the Machine Learning Tutorials repository! This repository contains tutorials and code examples for various machine learning algorithms implemented in Python.

Table of Contents

1. Introduction
2. Tutorials
3. Folder Structure
4. Contributing
5. License

Introduction

This repository aims to provide tutorials and code examples for various machine learning algorithms. Each tutorial covers a specific machine learning algorithm, explaining its concepts, implementation in Python, and providing code examples.

Tutorials

Here are the tutorials available in this repository:

• Basic Python - Introduction to basic Python programming concepts.

• Python Tutorial - In-depth tutorial covering essential Python concepts and features.

• CatBoost Tutorial - Tutorial and code examples for using CatBoost, a gradient boosting library.

• Decision Tree Tutorial - Tutorial and code examples for implementing decision tree algorithms.

• Dimensionality Reduction Algorithms Tutorial - Tutorial and code examples for dimensionality reduction algorithms.

• Gradient Boosting Machine Tutorial - Tutorial and code examples for implementing gradient boosting machine algorithms.

• K-Means Tutorial - Tutorial and code examples for implementing the K-means clustering algorithm.

• K-Nearest Neighbors Tutorial - Tutorial and code examples for implementing the K-nearest neighbors algorithm.

• LightGBM Tutorial - Tutorial and code examples for using LightGBM, a gradient boosting framework.

• Linear Regression Tutorial - Tutorial and code examples for implementing linear regression.

• Logistic Regression Tutorial - Tutorial and code examples for implementing logistic regression.

• Naive Bayes Tutorial - Tutorial and code examples for implementing naive Bayes classification.

• Random Forest Tutorial - Tutorial and code examples for implementing random forest algorithms.

• Support Vector Machine Tutorial - Tutorial and code examples for implementing support vector machine algorithms.

• XGBoost Tutorial - Tutorial and code examples for using XGBoost, an efficient and scalable implementation of gradient boosting.

Folder Structure

The repository follows a specific folder structure for organizing tutorials and code:

• basic_python/: Basic Python tutorial and related files.

• python_tutorial/: Python tutorial and related files.

• Algorithm-specific Python files (e.g., catboost.py, decision_tree.py, etc.).

Contributing

Contributions are welcome! If you'd like to contribute to this repository by adding new tutorials, improving existing ones, or fixing issues, please follow the contribution guidelines.

License

This repository is licensed under the MIT License.